
# importing python libraries
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import numpy as np
import geopandas as gpd
from shapely.geometry import Point, LineString
import matplotlib.pyplot as plt
from descartes import PolygonPatch


import pandas as pd


df = pd.read_csv("airports-extended.csv", 
                 header=None, 
                 names=["AirportID","Name","City",
                        "Country","IATA","ICAO","Latitude","Longitude","Altitude",
                        "Timezone","DST","TZ","Type","source"])
df


df.head(10)


df.describe()


df.info() # It is interesting to note that the statistical summary automatically discards any variable that is not numerical.

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12668 entries, 0 to 12667
Data columns (total 14 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   AirportID  12668 non-null  int64  
 1   Name       12668 non-null  object 
 2   City       12619 non-null  object 
 3   Country    12668 non-null  object 
 4   IATA       12668 non-null  object 
 5   ICAO       12667 non-null  object 
 6   Latitude   12668 non-null  float64
 7   Longitude  12668 non-null  float64
 8   Altitude   12668 non-null  int64  
 9   Timezone   12668 non-null  object 
 10  DST        12668 non-null  object 
 11  TZ         12668 non-null  object 
 12  Type       12668 non-null  object 
 13  source     12668 non-null  object 
dtypes: float64(2), int64(2), object(10)
memory usage: 1.4+ MB


df.describe()


df.describe(include="all")


df.describe().index

Index(['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'], dtype='object')


df_full = df.copy()


df.drop(["AirportID", "Latitude","Longitude","Altitude"], axis=1, inplace = True)
df


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12668 entries, 0 to 12667
Data columns (total 10 columns):
 #   Column    Non-Null Count  Dtype 
---  ------    --------------  ----- 
 0   Name      12668 non-null  object
 1   City      12619 non-null  object
 2   Country   12668 non-null  object
 3   IATA      12668 non-null  object
 4   ICAO      12667 non-null  object
 5   Timezone  12668 non-null  object
 6   DST       12668 non-null  object
 7   TZ        12668 non-null  object
 8   Type      12668 non-null  object
 9   source    12668 non-null  object
dtypes: object(10)
memory usage: 989.8+ KB


import matplotlib.pyplot as plt


plt.hist(df_full["Latitude"])
plt.xlabel("Latitude")
plt.ylabel("Frequency")
plt.title("Histogram of Latitude")
plt.show()


import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 8))

ax.scatter(df_full["Longitude"], df_full["Latitude"])
ax.set_xlabel("Longitude")
ax.set_ylabel("Latitude")
ax.set_title("Scatter Plot of Airport Locations")

plt.show()


plt.boxplot(df_full["Altitude"])
plt.xlabel("Altitude")
plt.ylabel("Meters")
plt.title("Box Plot of Altitude of the World Aerports")
plt.show()


missing_city = df[df["City"].isnull()]
missing_city


df.describe(include="object")


df.describe(include="all")


df.describe().index

Index(['count', 'unique', 'top', 'freq'], dtype='object')


df.describe()


prop_N = (df_full['IATA'] == '\\N').value_counts(normalize=True)
print(prop_N)

False    0.546574
True     0.453426
Name: IATA, dtype: float64


prop_N = (df_full['TZ'] == '\\N').value_counts(normalize=True)
print(prop_N)

False    0.661983
True     0.338017
Name: TZ, dtype: float64


prop_N = (df_full['ICAO'] == '\\N').value_counts(normalize=True)
print(prop_N)

False    0.644222
True     0.355778
Name: ICAO, dtype: float64


for col in df_full.columns:
    prop_N = (df_full[col] == '\\N').value_counts(normalize=True)
    print(col, prop_N)

AirportID False    1.0
Name: AirportID, dtype: float64
Name False    1.0
Name: Name, dtype: float64
City False    1.0
Name: City, dtype: float64
Country False    1.0
Name: Country, dtype: float64
IATA False    0.546574
True     0.453426
Name: IATA, dtype: float64
ICAO False    0.644222
True     0.355778
Name: ICAO, dtype: float64
Latitude False    1.0
Name: Latitude, dtype: float64
Longitude False    1.0
Name: Longitude, dtype: float64
Altitude False    1.0
Name: Altitude, dtype: float64
Timezone False    0.972135
True     0.027865
Name: Timezone, dtype: float64
DST False    0.972135
True     0.027865
Name: DST, dtype: float64
TZ False    0.661983
True     0.338017
Name: TZ, dtype: float64
Type False    0.869672
True     0.130328
Name: Type, dtype: float64
source False    0.869672
True     0.130328
Name: source, dtype: float64


df = pd.read_csv("airports-extended.csv", 
                 header=None, 
                 names=["AirportID","Name","City","Country","IATA","ICAO",
                        "Latitude","Longitude","Altitude","Timezone","DST","TZ","Type","source"],
                     na_values='\\N')


df.head(10)


df.drop(["AirportID", "Latitude","Longitude","Altitude"], axis=1, inplace=True)
df


df.isnull().sum()

Name           0
City          49
Country        0
IATA        5744
ICAO        4508
Timezone     353
DST          353
TZ          4282
Type        1651
source      1651
dtype: int64


df.IATA.fillna("UNKNOWN", inplace=True)
df.ICAO.fillna("UNKNOWN", inplace=True)
df.head(10)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12668 entries, 0 to 12667
Data columns (total 10 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Name      12668 non-null  object 
 1   City      12619 non-null  object 
 2   Country   12668 non-null  object 
 3   IATA      12668 non-null  object 
 4   ICAO      12668 non-null  object 
 5   Timezone  12315 non-null  float64
 6   DST       12315 non-null  object 
 7   TZ        8386 non-null   object 
 8   Type      11017 non-null  object 
 9   source    11017 non-null  object 
dtypes: float64(1), object(9)
memory usage: 989.8+ KB


df["DST"] = pd.Categorical(df["DST"])
df["TZ"] = pd.Categorical(df["TZ"])


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12668 entries, 0 to 12667
Data columns (total 10 columns):
 #   Column    Non-Null Count  Dtype   
---  ------    --------------  -----   
 0   Name      12668 non-null  object  
 1   City      12619 non-null  object  
 2   Country   12668 non-null  object  
 3   IATA      12668 non-null  object  
 4   ICAO      12668 non-null  object  
 5   Timezone  12315 non-null  float64 
 6   DST       12315 non-null  category
 7   TZ        8386 non-null   category
 8   Type      11017 non-null  object  
 9   source    11017 non-null  object  
dtypes: category(2), float64(1), object(7)
memory usage: 839.9+ KB


df.describe(include="category")


df.groupby(["Type"]).count()["Name"]

Type
airport    8264
port        101
station    1332
unknown    1320
Name: Name, dtype: int64


import pandas as pd
import matplotlib.pyplot as plt

# Group the dataframe by airport type, excluding "unknown" and "not identified"
grouped_df = df[~df["Type"].isin(["unknown"])].groupby("Type").size()

# Get the total number of each airport type
type_counts = df.groupby("Type").count()["Name"]

# Define the colors for the bars
colors = ["pink", "yellow", "cyan"]

# Create a bar chart of the type counts
plt.bar(grouped_df.index, grouped_df.values, color=colors)
plt.xlabel("Airport Type")
plt.ylabel("Count")
plt.title("Number of Airports by Type")

# Add total number of each airport type to the bar chart
for i, v in enumerate(grouped_df.values):
    plt.text(i, v+10, str(type_counts[grouped_df.index[i]]), color='black', ha='center')

plt.show()


df.loc[df["Type"] == "port", ["Name", "City", "Type"]]


df.loc[df["Country"] == "Spain", ["City", "Country", "Name"]]


df_spain = df.loc[(df["City"] == "Madrid") | (df["City"] == "Barcelona"), ["Country", "Name", "City"]]
df_spain


df_spain.to_csv("Madrid_Barcelona.csv", index=False, sep=",", encoding="utf8")


# importing python libraries
import urllib.request
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import os
import numpy as np
import plotly.express as px

# Libraries to make the graphs of latitude and longitude
import geopandas as gpd
from shapely.geometry import Point, LineString
import matplotlib.pyplot as plt
from descartes import PolygonPatch
import contextily as ctx
plt.rcParams['figure.dpi'] = 600

# Setting plots parameters
%matplotlib inline
sns.set_style('darkgrid')
matplotlib.rcParams['font.size'] = 1
matplotlib.rcParams['figure.figsize'] = (18, 10)
matplotlib.rcParams['figure.facecolor'] = '#00000000'
# 
import warnings
warnings.filterwarnings("ignore")


occurence_table_url = 'https://www.bst-tsb.gc.ca/includes/stats/csv/Air/ASISdb_MDOTW_VW_OCCURRENCE_PUBLIC.csv'


files = ['occurence.csv']
urls = [occurence_table_url]
encoding = 'ISO-8859-1'
for filename,url in zip(files,urls):
    if filename not in os.listdir():
        print('Downloading', filename)
        urllib.request.urlretrieve(url, filename)
        # in order to avoid formatting errors, we have to replace all "–" by "-"
    print('formatting ',filename)    
    # Read in the file
    with open(filename, 'r', encoding=encoding) as file:
        filedata = file.read()

    #Replace the target string
    filedata = filedata.replace('â\x80\x93', '-')

    # Write the file out again
    with open(filename, 'w', encoding=encoding) as file:
        file.write(filedata)

formatting  occurence.csv


occurence_df = pd.read_csv('occurence.csv',encoding = encoding)
print('Total number of columns', occurence_df.shape[0])

Total number of columns 50658


occurence_sel_columns = ['OccNo','CountryID_DisplayEng',
'ICAO_DisplayEng', 'OccDate', 'OccRegionID_DisplayEng', 'OccTime', 'OccTypeID_DisplayEng',
'TotalFatalCount','TotalSeriousCount',
'InjuriesEnum_DisplayEng', 'LightCondID_DisplayEng','SkyCondID_DisplayEng', 
'GeneralWeatherID_DisplayEng', 'VisibilyCeiling', 'Visibility', 'WeatherPhenomenaTypeID_DisplayEng', 'Summary',
'RunwaySurfaceID_DisplayEng', 'SurfaceContaminationID_DisplayEng','TimeZoneID_DisplayEng']


occurence_df = pd.read_csv('occurence.csv',encoding = encoding,usecols=occurence_sel_columns)


print('Number of aviation occurrences',occurence_df.shape[0])

Number of aviation occurrences 50658


# Librerias necesarias
import pandas as pd
import numpy as np
# Libraries to make the graphs of latitude and longitude
import geopandas as gpd
from shapely.geometry import Point, LineString
import matplotlib.pyplot as plt
from descartes import PolygonPatch

# Load a shapefile of world countries
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Define the latitude and longitude values of some points
latitudes = df_full[df_full['Country'] == 'Canada']['Latitude'] # base de datos de accidentes ['latitud']
longitudes = df_full[df_full['Country'] == 'Canada']['Longitude']
points = [Point(lon, lat) for lon, lat in zip(longitudes, latitudes)]

# Convert the points to a GeoDataFrame
geo_points = gpd.GeoDataFrame(geometry=points)

# Set up a plot of the world map
fig, ax = plt.subplots(figsize=(15, 10))
world.plot(ax=ax, edgecolor='k', linewidth = 0.5)

# Plot the points on the map
geo_points.plot(ax=ax, color='lime', marker = '^', markersize=3)
plt.grid(which = 'major', linestyle = ':')

# Set the x-axis limits to cover the range of -180 to 180
plt.xlim(-150, -50)

# Set the y-axis limits to cover the range of -90 to 90
plt.ylim(40, 90)

# Show the plot
plt.show()


total_airports_canada = (df["Country"] == "Canada").sum()
print("The total number of airports in Canada is:", total_airports_canada)

The total number of airports in Canada is: 640


import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

# Set the default size of the plots and font size
plt.rcParams['figure.figsize'] = (10, 8)
plt.rcParams['font.size'] = 19

# Define the column to group by
column = 'CountryID_DisplayEng'

# Group the occurrences by the selected column and count the number of occurrences per group
graph = occurence_df.groupby(column)['OccNo'].count().sort_values(ascending=False).head(20)

# Define the colors to use in the bar plot (rainbow colors)
colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']

# Create a bar plot of the grouped data, using the specified colors
fig = sns.barplot(x=graph.values, y=graph.index, palette=colors)

# Set the x-axis scale to logarithmic scale
fig.set_xscale('log')

# Set the labels and title of the chart
fig.set(xlabel='Number of occurrences', ylabel='', title='Number of events reported in each country')

# Set the tick marks and labels for the x-axis
fig.set_xticks([100, 1000, 10000])
fig.set_xticklabels([100, 1000, 10000])

# Show the chart
plt.show()


occurence_df['OccDate'] = pd.to_datetime(occurence_df['OccDate'])
occurence_df['OccYear'] = occurence_df['OccDate'].dt.year
occurence_df['OccMonth'] = occurence_df['OccDate'].dt.month
occurence_df['OccWeekday'] = occurence_df['OccDate'].dt.weekday


matplotlib.rcParams['figure.figsize'] = (10, 7)
column = 'OccYear'
# We use the data from the year 1965 because that is the year from which the data began to be recorded
data = occurence_df[occurence_df[column]>1965].dropna(subset=[column])
data2= data.groupby(column)['OccNo'].count()
plt.fill_between(data2.index, data2.values)
plt.xlabel('')
plt.ylabel('Number of occurrences')
plt.title('Number of occurrences reported each year')

Text(0.5, 1.0, 'Number of occurrences reported each year')


import matplotlib.pyplot as plt
import seaborn as sns

plt.rcParams['figure.figsize'] = (10, 5)

column = 'OccMonth'

# Generate a gradient of blue color
colors = sns.color_palette("Blues", n_colors=12)

fig = sns.countplot(x=column, data=occurence_df.dropna(subset=[column]), palette=colors)
fig.set(xlabel='', ylabel='Number of occurrences', title='Monthly breakdown of aviation occurrences')
months = ['Jan','Fev','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
fig.set_xticklabels(months)

plt.show()


import matplotlib.pyplot as plt
import seaborn as sns

matplotlib.rcParams['figure.figsize'] = (10, 5)

column = 'LightCondID_DisplayEng'

graph = occurence_df.groupby(column)['OccNo'].count().sort_values(ascending=False)

fig = sns.barplot(x=graph.values, y=graph.index, color='magenta')

fig.set(xlabel='Number of occurrences', ylabel='', title='Light condition impact on aviation occurrences')
plt.show()


import matplotlib.pyplot as plt
import seaborn as sns

# Set the size of the figure
plt.rcParams['figure.figsize'] = (10, 5)

# Select the column to group occurrences by
column = 'SkyCondID_DisplayEng'

# Group the occurrences by the selected column and count them
occurrences_by_sky = occurence_df.groupby(column)['OccNo'].count().sort_values(ascending=False)

# Create a bar plot of the number of occurrences for each sky condition
fig = sns.barplot(x=occurrences_by_sky.values, y=occurrences_by_sky.index, color='salmon')

# Set the x and y axis labels, as well as the title of the plot
fig.set(xlabel='Number of occurrences', ylabel='', title='Breakdown of aviation occurrences by sky conditions')

[Text(0.5, 0, 'Number of occurrences'),
 Text(0, 0.5, ''),
 Text(0.5, 1.0, 'Breakdown of aviation occurrences by sky conditions')]


import matplotlib.pyplot as plt
import seaborn as sns

# Set the figure size
plt.rcParams['figure.figsize'] = (10, 5)

# Define the column to analyze and filter out unknown values and missing data
column = 'GeneralWeatherID_DisplayEng'
data = occurence_df[occurence_df[column] != 'UNKNOWN'].dropna(subset=[column])

# Create the countplot with salmon and green colors
fig = sns.countplot(y=column, data=data, order=data[column].value_counts().index, palette=["salmon", "green"])

# Set the x and y labels and title for the plot
fig.set(xlabel='Events count', ylabel='', title='Breakdown of aviation occurrences by weather condition')

# Show the plot
plt.show()


import matplotlib.pyplot as plt
import seaborn as sns

# Set figure size
plt.rcParams['figure.figsize'] = (10, 10)

# Filter data by visibility and ceiling conditions
data = occurence_df[(occurence_df['VisibilyCeiling'] < 8000) & (occurence_df['Visibility'] < 6)]

# Group by visibility and ceiling and count occurrences
data = data.groupby(['VisibilyCeiling', 'Visibility'])['OccNo'].count()

# Create scatterplot
fig = sns.scatterplot(x='Visibility', y='VisibilyCeiling', hue=data.values, data=data, s=100)

# Set axis labels and title
fig.set(xlabel='Horizontal visibility (miles)', ylabel='Vertical visibility (ft)', title='Impact of visibility on aviation safety')

# Set legend title
fig.legend(title='Number of reported events')

<matplotlib.legend.Legend at 0x1bf84903b50>


import matplotlib.pyplot as plt
import seaborn as sns

# Set the figure size
plt.rcParams['figure.figsize'] = (10, 5)

# Define the column to analyze and get the top 7 values
column = 'WeatherPhenomenaTypeID_DisplayEng'
graph = occurence_df.groupby(column)['OccNo'].count().sort_values(ascending=False).head(7)

# Create the barplot with random colors
fig = sns.barplot(x=graph.values, y=graph.index, palette=['#F9A03F', '#C9E265', '#5EC1C0', '#F97B72', '#C355F5', '#5EC17A', '#F9E03F'])

# Set the x and y labels and title for the plot
fig.set(xlabel='Number of occurrences [longitude scale]', ylabel='', title='Impact of weather phenomena on flight safety')
fig.set_xscale('log')
fig.set_xticks([10, 100, 1000])
fig.set_xticklabels([10, 100, 1000])

# Show the plot
plt.show()


matplotlib.rcParams['figure.figsize'] = (10, 5)
column = 'OccYear'
data = occurence_df[occurence_df[column]>1965].dropna(subset=[column])
data2= data.groupby(column)['TotalFatalCount'].sum()
plt.fill_between(data2.index, data2.values);
plt.ylabel('')
plt.title('Number of fatal injuries reported each year')

Text(0.5, 1.0, 'Number of fatal injuries reported each year')


matplotlib.rcParams['figure.figsize'] = (10, 5)
column = 'OccYear'
data = occurence_df[occurence_df[column]>1965].dropna(subset=[column])
data2= data.groupby(column)['TotalSeriousCount'].sum()
plt.fill_between(data2.index, data2.values)
plt.ylabel('')
plt.title('Number of serious injuries reported each year')

Text(0.5, 1.0, 'Number of serious injuries reported each year')


df = pd.read_csv('occurence.csv', low_memory=False)
df_airports = pd.read_csv('airports-extended.csv', header = None, encoding = 'utf-8')
df_airports.columns = ['AirportID','Name', 'City', 'Country', 'IATA',
                       'ICAO', 'Latitude', 'Longitude', 'Altitude',
                       'Timezone', 'DST', 'Tz', 'Type', 'Source']


df['ICAO']

0         NaN
1        CYYZ
2        CYYT
3        CYVV
4        CYXL
         ... 
50653     NaN
50654     NaN
50655     NaN
50656     NaN
50657     NaN
Name: ICAO, Length: 50658, dtype: object


canada = df_airports[df_airports['Country'] == 'Canada'].copy()


new = pd.merge(canada, df, on = 'ICAO', suffixes = ['_airport', '_accident'])


# Load a shapefile of world countries
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Define the latitude and longitude values of some points
latitudes = new['Latitude_accident'] # base de datos de accidentes ['latitud']
longitudes = -new['Longitude_accident']
points = [Point(lon, lat) for lon, lat in zip(longitudes, latitudes)]

# Convert the points to a GeoDataFrame
geo_points = gpd.GeoDataFrame(geometry=points)

# Set up a plot of the world map
fig, ax = plt.subplots(figsize=(15, 10))
world.plot(ax=ax, edgecolor='k', linewidth = 0.5)

# Plot the points on the map
geo_points.plot(ax=ax, color='red', marker = '^', markersize=3)
plt.grid(which = 'major', linestyle = ':')

# Set the x-axis limits to cover the range of -180 to 180
plt.xlim(-150, -50)

# Set the y-axis limits to cover the range of -90 to 90
plt.ylim(40, 90)

# Show the plot
plt.show()


# Load a shapefile of world countries
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Define the latitude and longitude values of some points
latitudes_acc = new['Latitude_accident'] # database of accidents ['Latitude]
longitudes_acc = -new['Longitude_accident'] # database of accidents [Longitude]
points_acc = [Point(lon, lat) for lon, lat in zip(longitudes_acc, latitudes_acc)]

latitudes_air = new['Latitude_airport'] # database of accidents ['Latitude]
longitudes_air = new['Longitude_airport'] # database of accidents [Longitude]
points_air = [Point(lon, lat) for lon, lat in zip(longitudes_air, latitudes_air)]

# Convert the points to a GeoDataFrame

geo_points_acc = gpd.GeoDataFrame(geometry=points_acc)

geo_points_air = gpd.GeoDataFrame(geometry=points_air)
# Set up a plot of the world map


fig, ax = plt.subplots(figsize=(15, 10))


world.plot(ax=ax, edgecolor='k', linewidth = 0.5)

# Plot the points on the map
geo_points_acc.plot(ax=ax, color='red', marker = '8', markersize=3)


geo_points_air.plot(ax=ax, color='lime', marker = '^', markersize=3)



plt.grid(which = 'major', linestyle = ':')

# Set the x-axis limits to cover the range of -180 to 180
plt.xlim(-150, -50)

# Set the y-axis limits to cover the range of -90 to 90
plt.ylim(40, 90)


# Show the plot
plt.show()

	AirportID	Name	City	Country	IATA	ICAO	Latitude	Longitude	Altitude	Timezone	DST	TZ	Type	source
0	1	Goroka Airport	Goroka	Papua New Guinea	GKA	AYGA	-6.081690	145.391998	5282	10	U	Pacific/Port_Moresby	airport	OurAirports
1	2	Madang Airport	Madang	Papua New Guinea	MAG	AYMD	-5.207080	145.789001	20	10	U	Pacific/Port_Moresby	airport	OurAirports
2	3	Mount Hagen Kagamuga Airport	Mount Hagen	Papua New Guinea	HGU	AYMH	-5.826790	144.296005	5388	10	U	Pacific/Port_Moresby	airport	OurAirports
3	4	Nadzab Airport	Nadzab	Papua New Guinea	LAE	AYNZ	-6.569803	146.725977	239	10	U	Pacific/Port_Moresby	airport	OurAirports
4	5	Port Moresby Jacksons International Airport	Port Moresby	Papua New Guinea	POM	AYPY	-9.443380	147.220001	146	10	U	Pacific/Port_Moresby	airport	OurAirports
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
12663	14107	Ulan-Ude East Airport	Ulan Ude	Russia	\N	XIUW	51.849998	107.737999	1670	\N	\N	\N	airport	OurAirports
12664	14108	Krechevitsy Air Base	Novgorod	Russia	\N	ULLK	58.625000	31.385000	85	\N	\N	\N	airport	OurAirports
12665	14109	Desierto de Atacama Airport	Copiapo	Chile	CPO	SCAT	-27.261200	-70.779198	670	\N	\N	\N	airport	OurAirports
12666	14110	Melitopol Air Base	Melitopol	Ukraine	\N	UKDM	46.880001	35.305000	0	\N	\N	\N	airport	OurAirports
12667	14111	Lincoln Train Station LNK	Lincoln	United States	\N	\N	40.815833	-96.713889	1176	-5	A	\N	\N	\N

	AirportID	Name	City	Country	IATA	ICAO	Latitude	Longitude	Altitude	Timezone	DST	TZ	Type	source
0	1	Goroka Airport	Goroka	Papua New Guinea	GKA	AYGA	-6.081690	145.391998	5282	10	U	Pacific/Port_Moresby	airport	OurAirports
1	2	Madang Airport	Madang	Papua New Guinea	MAG	AYMD	-5.207080	145.789001	20	10	U	Pacific/Port_Moresby	airport	OurAirports
2	3	Mount Hagen Kagamuga Airport	Mount Hagen	Papua New Guinea	HGU	AYMH	-5.826790	144.296005	5388	10	U	Pacific/Port_Moresby	airport	OurAirports
3	4	Nadzab Airport	Nadzab	Papua New Guinea	LAE	AYNZ	-6.569803	146.725977	239	10	U	Pacific/Port_Moresby	airport	OurAirports
4	5	Port Moresby Jacksons International Airport	Port Moresby	Papua New Guinea	POM	AYPY	-9.443380	147.220001	146	10	U	Pacific/Port_Moresby	airport	OurAirports
5	6	Wewak International Airport	Wewak	Papua New Guinea	WWK	AYWK	-3.583830	143.669006	19	10	U	Pacific/Port_Moresby	airport	OurAirports
6	7	Narsarsuaq Airport	Narssarssuaq	Greenland	UAK	BGBW	61.160500	-45.425999	112	-3	E	America/Godthab	airport	OurAirports
7	8	Godthaab / Nuuk Airport	Godthaab	Greenland	GOH	BGGH	64.190903	-51.678101	283	-3	E	America/Godthab	airport	OurAirports
8	9	Kangerlussuaq Airport	Sondrestrom	Greenland	SFJ	BGSF	67.012222	-50.711603	165	-3	E	America/Godthab	airport	OurAirports
9	10	Thule Air Base	Thule	Greenland	THU	BGTL	76.531197	-68.703201	251	-4	E	America/Thule	airport	OurAirports

	AirportID	Latitude	Longitude	Altitude
count	12668.000000	12668.000000	12668.000000	12668.000000
mean	7311.584465	29.820510	-1.041145	831.871093
std	4202.520098	26.923942	83.618663	1498.239733
min	1.000000	-90.000000	-179.876999	-1266.000000
25%	3349.750000	16.115875	-77.294770	38.000000
50%	7771.500000	38.341850	6.955411	232.500000
75%	10943.250000	49.306042	47.835824	883.500000
max	14111.000000	90.000000	179.951004	14472.000000

	AirportID	Latitude	Longitude	Altitude
count	12668.000000	12668.000000	12668.000000	12668.000000
mean	7311.584465	29.820510	-1.041145	831.871093
std	4202.520098	26.923942	83.618663	1498.239733
min	1.000000	-90.000000	-179.876999	-1266.000000
25%	3349.750000	16.115875	-77.294770	38.000000
50%	7771.500000	38.341850	6.955411	232.500000
75%	10943.250000	49.306042	47.835824	883.500000
max	14111.000000	90.000000	179.951004	14472.000000

	Name	City	Country	IATA	ICAO	Timezone	DST	TZ	Type	source
0	Goroka Airport	Goroka	Papua New Guinea	GKA	AYGA	10	U	Pacific/Port_Moresby	airport	OurAirports
1	Madang Airport	Madang	Papua New Guinea	MAG	AYMD	10	U	Pacific/Port_Moresby	airport	OurAirports
2	Mount Hagen Kagamuga Airport	Mount Hagen	Papua New Guinea	HGU	AYMH	10	U	Pacific/Port_Moresby	airport	OurAirports
3	Nadzab Airport	Nadzab	Papua New Guinea	LAE	AYNZ	10	U	Pacific/Port_Moresby	airport	OurAirports
4	Port Moresby Jacksons International Airport	Port Moresby	Papua New Guinea	POM	AYPY	10	U	Pacific/Port_Moresby	airport	OurAirports
...	...	...	...	...	...	...	...	...	...	...
12663	Ulan-Ude East Airport	Ulan Ude	Russia	\N	XIUW	\N	\N	\N	airport	OurAirports
12664	Krechevitsy Air Base	Novgorod	Russia	\N	ULLK	\N	\N	\N	airport	OurAirports
12665	Desierto de Atacama Airport	Copiapo	Chile	CPO	SCAT	\N	\N	\N	airport	OurAirports
12666	Melitopol Air Base	Melitopol	Ukraine	\N	UKDM	\N	\N	\N	airport	OurAirports
12667	Lincoln Train Station LNK	Lincoln	United States	\N	\N	-5	A	\N	\N	\N

	AirportID	Name	City	Country	IATA	ICAO	Latitude	Longitude	Altitude	Timezone	DST	TZ	Type	source
count	12668.000000	12668	12619	12668	12668	12667	12668.000000	12668.000000	12668.000000	12668	12668	12668	12668	12668
unique	NaN	12330	9673	241	6925	8161	NaN	NaN	NaN	42	8	312	5	4
top	NaN	North Sea	New York	United States	\N	\N	NaN	NaN	NaN	1	E	\N	airport	OurAirports
freq	NaN	21	50	2708	5744	4507	NaN	NaN	NaN	2706	3478	4282	8264	7698
mean	7311.584465	NaN	NaN	NaN	NaN	NaN	29.820510	-1.041145	831.871093	NaN	NaN	NaN	NaN	NaN
std	4202.520098	NaN	NaN	NaN	NaN	NaN	26.923942	83.618663	1498.239733	NaN	NaN	NaN	NaN	NaN
min	1.000000	NaN	NaN	NaN	NaN	NaN	-90.000000	-179.876999	-1266.000000	NaN	NaN	NaN	NaN	NaN
25%	3349.750000	NaN	NaN	NaN	NaN	NaN	16.115875	-77.294770	38.000000	NaN	NaN	NaN	NaN	NaN
50%	7771.500000	NaN	NaN	NaN	NaN	NaN	38.341850	6.955411	232.500000	NaN	NaN	NaN	NaN	NaN
75%	10943.250000	NaN	NaN	NaN	NaN	NaN	49.306042	47.835824	883.500000	NaN	NaN	NaN	NaN	NaN
max	14111.000000	NaN	NaN	NaN	NaN	NaN	90.000000	179.951004	14472.000000	NaN	NaN	NaN	NaN	NaN

	Name	City	Country	IATA	ICAO	Timezone	DST	TZ	Type	source
10351	Minsk Mazowiecki Military Air Base	NaN	Poland	\N	EPMM	\N	\N	\N	airport	OurAirports
10352	Powidz Military Air Base	NaN	Poland	\N	EPPW	\N	\N	\N	airport	OurAirports
10457	King Salman Abdulaziz Airport	NaN	Saudi Arabia	DWD	OEDM	\N	\N	\N	airport	OurAirports
10458	King Khaled Air Base	NaN	Saudi Arabia	KMX	OEKM	\N	\N	\N	airport	OurAirports
10478	Asahikawa Airfield	NaN	Japan	\N	RJCA	\N	\N	\N	airport	OurAirports
10480	Utsunomiya Airport	NaN	Japan	\N	RJTU	\N	\N	\N	airport	OurAirports
10481	Jungwon Air Base/Chungju Airport	NaN	South Korea	\N	RKTI	\N	\N	\N	airport	OurAirports
10484	Bislig Airport	NaN	Philippines	BPH	RPMF	\N	\N	\N	airport	OurAirports
10485	Mati National Airport	NaN	Philippines	MXI	RPMQ	\N	\N	\N	airport	OurAirports
10504	Metropolitano Airport	NaN	Venezuela	\N	SVMP	\N	\N	\N	airport	OurAirports
10511	Belaya Gora Airport	NaN	Russia	BGN	UESG	\N	\N	\N	airport	OurAirports
10541	Volgodonsk Airport	NaN	Russia	VLK	URRY	\N	\N	\N	airport	OurAirports
10553	Ratnagiri Airport	NaN	India	RTC	VARG	\N	\N	\N	airport	OurAirports
10556	Ambala Air Force Station	NaN	India	\N	VIAM	\N	\N	\N	airport	OurAirports
10557	Sirsa Air Force Station	NaN	India	\N	VISA	\N	\N	\N	airport	OurAirports
10558	Udhampur Air Force Station	NaN	India	\N	VIUX	\N	\N	\N	airport	OurAirports
10569	Ararat Airport	NaN	Australia	ARY	YARA	\N	\N	\N	airport	OurAirports
10570	Benalla Airport	NaN	Australia	BLN	YBLA	\N	\N	\N	airport	OurAirports
10571	Balranald Airport	NaN	Australia	BZD	YBRN	\N	\N	\N	airport	OurAirports
10572	Brewarrina Airport	NaN	Australia	BWQ	YBRW	\N	\N	\N	airport	OurAirports
10573	Cleve Airport	NaN	Australia	CVC	YCEE	\N	\N	\N	airport	OurAirports
10574	Corowa Airport	NaN	Australia	CWW	YCOR	\N	\N	\N	airport	OurAirports
10575	Corryong Airport	NaN	Australia	CYG	YCRG	\N	\N	\N	airport	OurAirports
10576	Cootamundra Airport	NaN	Australia	CMD	YCTM	\N	\N	\N	airport	OurAirports
10577	Dirranbandi Airport	NaN	Australia	DRN	YDBI	\N	\N	\N	airport	OurAirports
10579	Dysart Airport	NaN	Australia	DYA	YDYS	\N	\N	\N	airport	OurAirports
10580	Echuca Airport	NaN	Australia	ECH	YECH	\N	\N	\N	airport	OurAirports
10582	Gunnedah Airport	NaN	Australia	GUH	YGDH	\N	\N	\N	airport	OurAirports
10583	Hay Airport	NaN	Australia	HXX	YHAY	\N	\N	\N	airport	OurAirports
10584	Hopetoun Airport	NaN	Australia	HTU	YHPN	\N	\N	\N	airport	OurAirports
10585	Kerang Airport	NaN	Australia	KRA	YKER	\N	\N	\N	airport	OurAirports
10586	Kempsey Airport	NaN	Australia	KPS	YKMP	\N	\N	\N	airport	OurAirports
10587	Kingaroy Airport	NaN	Australia	KGY	YKRY	\N	\N	\N	airport	OurAirports
10589	Mareeba Airport	NaN	Australia	MRG	YMBA	\N	\N	\N	airport	OurAirports
10590	Ngukurr Airport	NaN	Australia	RPM	YNGU	\N	\N	\N	airport	OurAirports
10591	Narromine Airport	NaN	Australia	QRM	YNRM	\N	\N	\N	airport	OurAirports
10592	Port Pirie Airport	NaN	Australia	PPI	YPIR	\N	\N	\N	airport	OurAirports
10593	Smithton Airport	NaN	Australia	SIO	YSMI	\N	\N	\N	airport	OurAirports
10594	Snake Bay Airport	NaN	Australia	SNB	YSNB	\N	\N	\N	airport	OurAirports
10595	Stawell Airport	NaN	Australia	SWC	YSWL	\N	\N	\N	airport	OurAirports
10596	Tibooburra Airport	NaN	Australia	TYB	YTIB	\N	\N	\N	airport	OurAirports
10597	Tumut Airport	NaN	Australia	TUM	YTMU	\N	\N	\N	airport	OurAirports
10598	Wangaratta Airport	NaN	Australia	WGT	YWGT	\N	\N	\N	airport	OurAirports
10599	Warracknabeal Airport	NaN	Australia	WKB	YWKB	\N	\N	\N	airport	OurAirports
10600	Warren Airport	NaN	Australia	QRR	YWRN	\N	\N	\N	airport	OurAirports
10603	Young Airport	NaN	Australia	NGA	YYNG	\N	\N	\N	airport	OurAirports
10608	Baruun Urt Airport	NaN	Mongolia	UUN	ZMBU	\N	\N	\N	airport	OurAirports
12278	Hard Bargain Airport	NaN	Bahamas	\N	MYLR	\N	\N	\N	airport	OurAirports
12649	Xingcheng Air Base	NaN	China	XEN	ZYXC	\N	\N	\N	airport	OurAirports

	Name	City	Type
6074	Macau Ferry Pier	Macau	port
6438	Stockholm Cruise Port	Stockholm	port
6439	Helsingborg Cruise Port	Helsingborg	port
6444	Helsinki Cruise Port	Helsinki	port
6565	Sakaiminato Port	Sakaiminato	port
...	...	...	...
9948	Ludington Ferry Terminal	Ludington	port
9949	Manitowoc Ferry Terminal	Manitowoc	port
10184	Fuyong Ferry Terminal	Shenzhen	port
10207	Osaka Port International Ferry Terminal	Osaka	port
10208	Hakata Port International Terminal	Fukuoka	port

	City	Country	Name
1030	Fuerteventura	Spain	Fuerteventura Airport
1031	Hierro	Spain	Hierro Airport
1032	Santa Cruz De La Palma	Spain	La Palma Airport
1033	Gran Canaria	Spain	Gran Canaria Airport
1034	Arrecife	Spain	Lanzarote Airport
...	...	...	...
11437	Madrid	Spain	Madrid Estacion Sur
11438	Granada	Spain	Granada Bus Station
11879	Palma de Mallorca	Spain	Palma de Mallorca Cruise Terminal
12234	Algeciras	Spain	Algeciras Heliport
12386	Murcia	Spain	Región de Murcia International Airport

	Country	Name	City
1188	Spain	Barcelona International Airport	Barcelona
1194	Spain	Getafe Air Base	Madrid
1198	Spain	Adolfo Suárez Madrid–Barajas Airport	Madrid
1214	Spain	Torrejón Airport	Madrid
1217	Spain	Cuatro Vientos	Madrid
2690	Venezuela	General José Antonio Anzoategui International ...	Barcelona
6302	Spain	Barcelona Cruise Terminal	Barcelona
8046	Spain	Atocha	Madrid
8053	Spain	Chamartin Station	Madrid
8055	Spain	Aeroport	Barcelona
8056	Spain	Passeig de Gracia	Barcelona
8057	Spain	Sants	Barcelona
8846	Spain	Barajas Station	Madrid
9156	Spain	Aeropuerto T4 Madrid Metro	Madrid
10033	Spain	Estacio de Franca	Barcelona
10264	Spain	Placa d Espanya Station	Barcelona
10435	Spain	Cuatro Vientos Airport	Madrid
11435	Spain	Barcelona-Sants	Barcelona
11437	Spain	Madrid Estacion Sur	Madrid

Individual Project Airports¶

Aviation and its Data¶

Introduction¶

Content of this notebook¶

We will investigate the following points from openflights.org:¶

We will investigate the following points from Aviation occurences in Canada :¶

1. Loading of the dataset as a dataframe.¶

2. Lets show the first 10 rows of the dataframe.¶

3. We are going to get a statistical summary that can help us to understand the dataset and identify potential issues or areas for further investigation.¶

4. For this analysis we are not going to use the 'AirportID', 'Latitude', 'Longitude' and 'Altitude' columns, so we will remove them from the dataframe.¶

5. Get a statistical summary again, how has the data changed?¶

6. In the statistical summary above it seems that in column TZ there is a rare value \N, check the proportion of them with value_counts.¶

7. Reload the dataset so that null values are correctly interpreted (repeat section 4, delete columns).¶

8.Check the entire dataframe for null values.¶

9. Overwrites the null values of the IATA and ICAO columns with the value 'UNKNOWN'¶

10. Changes the type of the DST and TZ variables to categorical.¶

11. Obtain a statistical summary of the categorical variables.¶

12. Groups the dataframe by airport type, showing the type count.¶

13. Select the name of the cities whose airport type is "port"¶

14. Shows all the rows of the fields name of the airport, name of the country and, name of the city, whose country is Spain.¶

15. Shows the name of the country and the airport belonging to the city of Madrid and Barcelona. Are all the records from Spain?¶

16. Save the previous results in a csv called Madrid_Barcelona.csv¶

Columns descriptions:¶

Air Occurences in Canada¶

Introduction¶

Content of this notebook¶

Datasets used in this study¶

1st step: download the dataset¶

The airports of Canada¶

When did the incidents happen?¶

Are there any periods of the year where more events take place?¶

Impact of environmental factors on aviation safety¶

Breakdown of aviation occurrences by sky conditions¶

Breakdown of aviation occurrences by weather condition¶

Impact of visibility on aviation safety¶

Number of fatal injuries reported each year¶

Number of serious injuries reported each year¶

Visualizing the total number of accidents¶

Visualization of airplane crashes¶

Columns descriptions:¶

Summary¶

Web references¶